2. Applications of Graph Data Augmentationin Deep Graph Learning GraphDA 主要是为了 optimal graph learning 和 low-resourcegraph learning 问题。 2.1 GraphDA for Optimal Graph Learning 分为两级:Optimal Structure Learning,Optimal Feature Learning 2.1.1 Optimal Structure Learning Optimal Structure Learning ...
论文标题:Data Augmentation for Deep Graph Learning: A Survey 论文作者:Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu 论文来源:2022, arXiv 论文地址:download 1 介绍 本文主要总结图数据增强,并对该领域的代表性方法做出归类分析。 DGL 存在的两个问题: ...
1. introduction 本文的贡献如下: (1)本文是GraphDA(图数据增强)的第一篇survey。 (2)我们提出了根据目标增强方法(i.e. feature-oriented, structure-oriented, lable-oriented)GraphDA的一种分类。 (3)讨论了GDA的应用来解决data-centric DGL的两个主要的研究问题:optimal graph learning和low-resource graph lea...
Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the ab initio calculations required by CSPA limits its u
Task‑specific augmentation for NLP NLP的任务特异性增强 Self‑supervised learning and data augmentation 自我监督学习和数据扩充 Transfer and multi‑task learning 迁移和多任务学习 AI‑GAs 人工智能生成算法 Conclusion 背景 对图像进行语义保留增强很容易,但在文本领域要做到这一点要困难得多。
Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundarie...
To build useful Deep Learning models, the validation error must continue to decrease with the training error. Data Augmentation is a very powerful method of achieving this. The augmented data will represent a more comprehensive set of possible data points, thus minimizing the distance between the ...
deep-learning data-augmentation-strategies overfitting video-analysis temporal-action-localization Updated Dec 24, 2021 Python nis-research / dfmX-augmentation Star 3 Code Issues Pull requests Augmentation for CV using frequency shortcuts data-augmentation-strategies data-augmentation frequency-analysis...
Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-... A Munawar,P Vinayavekhin,GD Magistris - ...
DATA augmentationDEEP learningCARTESIAN coordinatesFront Cover: Rotational Variance-Based Data Augmentation in 3D Graph Convolutional Network (Chem. Keywords: Data augmentation; Deep learning; 3D Graph convolutional network; Protein-ligand binding; Rotational variance EN Data augmentation Deep learning 3D ...